Staff Data Scientist - Wildfire
- Canada
Full-time
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Job details
- Job type
- Full-time
- Experience
- 8+ years of applied research or data science experience in wildfire science, fire ecology, forestry, remote sensing, atmospheric science, or a related quantitative field
- Posting language
- English
- Posted
Original job description
Role & Team
We are excited to add a Staff Data Scientist, Wildfire to our team. This individual will lead the scientific foundation of our Fuel Detection Model, the core engine that translates satellite and environmental data into an understanding of vegetation structure, fuel loads, and wildfire risk.
Working alongside ML engineers, and a product team, you'll define accuracy for our models, design the research and validation methods that prove it, and ensure our modeling choices are grounded in fire science and remote sensing fundamentals. This is a great opportunity for someone who is energized by open scientific questions with direct real-world stakes, and who wants their research to shape how utilities prevent catastrophic wildfires.
Time Zone Requirement: North America (NST, AST, EST, CST, MST, PST)
What You’ll Do
What We Offer
We are excited to add a Staff Data Scientist, Wildfire to our team. This individual will lead the scientific foundation of our Fuel Detection Model, the core engine that translates satellite and environmental data into an understanding of vegetation structure, fuel loads, and wildfire risk.
Working alongside ML engineers, and a product team, you'll define accuracy for our models, design the research and validation methods that prove it, and ensure our modeling choices are grounded in fire science and remote sensing fundamentals. This is a great opportunity for someone who is energized by open scientific questions with direct real-world stakes, and who wants their research to shape how utilities prevent catastrophic wildfires.
Time Zone Requirement: North America (NST, AST, EST, CST, MST, PST)
What You’ll Do
- Lead research into how vegetation structure, fuel conditions, and wildfire risk can be estimated from satellite, LiDAR, and environmental data across diverse geographies
- Design rigorous validation and evaluation methodologies, including ground-truth strategies, uncertainty quantification, and error analysis tied to real-world impact
- Prototype and refine ML modeling approaches, then partner with ML engineers to translate them into production systems
- Integrate established fire science, such as fuel models and fire behavior frameworks, with data-driven methods
- Define scientific standards for experimentation, reproducibility, and model interpretability across the modeling organization
- Communicate research findings clearly to engineers, product teams, customers, and the broader wildfire science community
- Mentor ML engineers on scientific methodology and domain reasoning
- 8+ years of applied research or data science experience in wildfire science, fire ecology, forestry, remote sensing, atmospheric science, or a related quantitative field
- Deep expertise in remote sensing and geospatial analysis, including working with satellite imagery and large-scale environmental datasets
- Strong statistical modeling and machine learning skills in Python, with tools like GeoPandas, scikit-learn, PyTorch, or XGBoost
- Track record of designing validation studies and evaluation frameworks for environmental or geospatial models
- Excellent communication skills, with the ability to make complex scientific work legible across technical and non-technical audience
- Familiarity with fire behavior or fuels modeling frameworks (e.g., Rothermel-based models, LANDFIRE fuel classifications)
- Experience integrating physics-based models with ML, or with active learning and uncertainty quantification
- Peer-reviewed publications in wildfire science, remote sensing, or environmental modeling
- Familiarity with GCP, Vertex AI, or similar cloud-based platforms
- Experience in remote-first or globally distributed team
What We Offer
- Competitive, location-specific compensation and benefits
- Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
- Home office stipend, co-working and ongoing education budgets
- A company culture that genuinely embodies each of our core values
- To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis
More about this job
Responsibilities
Lead research and develop rigorous validation methods for models that estimate vegetation structure, fuel conditions, and wildfire risk from satellite, LiDAR, and environmental data. Partner with engineers and product teams to productionize models, establish scientific standards, communicate findings, and mentor colleagues.
Requirements
Requires 8+ years of applied research or data science experience in wildfire science or a related quantitative field, with deep expertise in remote sensing, geospatial analysis, statistical modeling, and machine learning in Python. Candidates should have a track record designing validation frameworks and be able to communicate complex scientific work to technical and non-technical audiences.
Skills
- Remote Sensing
- Geospatial Analysis
- Satellite Imagery
- LiDAR
- Wildfire Science
- Fire Ecology
- Forestry
- Statistical Modeling
- Machine Learning
- Python
- Model Validation
- Uncertainty Quantification
- Error Analysis
- Fire Behavior Modeling
- Scientific Communication
- Mentoring
Benefits
- Location-Specific Compensation
- Flexible Working Environment
- Home Office Stipend
- Co-Working Budget
- Ongoing Education Budget
Visa sponsorship
Not detected in the job text
Categories
- Science & Research
- Environmental & Sustainability
- Data & Analytics
- Technology
- Software
Keywords
- Fuel Detection Model
- Wildfire Risk
- Vegetation Structure
- Fuel Loads
- Satellite Imagery
- LiDAR
- Environmental Data
- Remote Sensing
- Geospatial Analysis
- Fire Science
- Fire Ecology
- Forestry
- Atmospheric Science
- Python
- GeoPandas
- Scikit-Learn
- PyTorch
- XGBoost
- Statistical Modeling
- Machine Learning
- Ground-Truth Strategies
- Uncertainty Quantification
- Error Analysis
- Model Interpretability
- Rothermel-Based Models
- LANDFIRE Fuel Classifications
- Active Learning
- GCP
- Vertex AI
- Reproducibility